


import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__    :Dict       =				logging.getLogger()

def 			lowerCAmelCase__					(     )    ->						Dict:


							'''simple docstring'''





							_UpperCAmelCase											=     argparse.ArgumentParser()
							parser.add_argument('-f'			)
							_UpperCAmelCase											=     parser.parse_args()
							return args.f

def 			lowerCAmelCase__					(     a__:						str			)    ->						Tuple:


							'''simple docstring'''





							_UpperCAmelCase											=     {}
							_UpperCAmelCase											=     os.path.join(a__     ,    'all_results.json'			)
							if os.path.exists(a__			):
														with open(a__     ,    'r'			) as f:
																					_UpperCAmelCase											=     json.load(a__			)
							else:
														raise ValueError(F'''can\'t find {path}'''			)
							return results

def 			lowerCAmelCase__					(     )    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     torch.cuda.is_available() and torch_device == """cuda"""
							return is_using_cuda and is_apex_available()


lowerCAmelCase__    :List[str]       =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 						__a (							a_     ):
		@classmethod
		def       UpperCAmelCase__							(     cls		)					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     tempfile.mkdtemp()
									_UpperCAmelCase											=     os.path.join(cls.tmpdir     ,       'default_config.yml'		)
									write_basic_config(save_location=cls.configPath		)
									_UpperCAmelCase											=     ["""accelerate""", """launch""", """--config_file""", cls.configPath]
		@classmethod
		def       UpperCAmelCase__							(     cls		)					->			Optional[Any]:




									"""simple docstring"""





									shutil.rmtree(cls.tmpdir		)
		@mock.patch.dict(os.environ     ,       {'WANDB_MODE': 'offline'}		)
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_auto_remove_tmp_dir()
									_UpperCAmelCase											=     f'''
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

									if is_cuda_and_apex_available():
																testargs.append('--fp16'		)

									run_command(self._launch_args + testargs		)
									_UpperCAmelCase											=     get_results(_lowercase		)
									self.assertGreaterEqual(result['eval_accuracy']     ,       0.75		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'epoch_0'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'glue_no_trainer'		)		)		)
		@mock.patch.dict(os.environ     ,       {'WANDB_MODE': 'offline'}		)
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_auto_remove_tmp_dir()
									_UpperCAmelCase											=     f'''
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

									if torch.cuda.device_count() > 1:
																# Skipping because there are not enough batches to train the model + would need a drop_last to work.
																return

									run_command(self._launch_args + testargs		)
									_UpperCAmelCase											=     get_results(_lowercase		)
									self.assertLess(result['perplexity']     ,       100		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'epoch_0'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'clm_no_trainer'		)		)		)
		@mock.patch.dict(os.environ     ,       {'WANDB_MODE': 'offline'}		)
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_auto_remove_tmp_dir()
									_UpperCAmelCase											=     f'''
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

									run_command(self._launch_args + testargs		)
									_UpperCAmelCase											=     get_results(_lowercase		)
									self.assertLess(result['perplexity']     ,       42		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'epoch_0'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'mlm_no_trainer'		)		)		)
		@mock.patch.dict(os.environ     ,       {'WANDB_MODE': 'offline'}		)
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     7 if get_gpu_count() > 1 else 2

									_UpperCAmelCase											=     self.get_auto_remove_tmp_dir()
									_UpperCAmelCase											=     f'''
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

									run_command(self._launch_args + testargs		)
									_UpperCAmelCase											=     get_results(_lowercase		)
									self.assertGreaterEqual(result['eval_accuracy']     ,       0.75		)
									self.assertLess(result['train_loss']     ,       0.5		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'epoch_0'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'ner_no_trainer'		)		)		)
		@unittest.skip(reason='Fix me @muellerzr'		)
		@mock.patch.dict(os.environ     ,       {'WANDB_MODE': 'offline'}		)
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_auto_remove_tmp_dir()
									_UpperCAmelCase											=     f'''
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

									run_command(self._launch_args + testargs		)
									_UpperCAmelCase											=     get_results(_lowercase		)
									# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
									self.assertGreaterEqual(result['eval_f1']     ,       28		)
									self.assertGreaterEqual(result['eval_exact']     ,       28		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'epoch_0'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'qa_no_trainer'		)		)		)
		@mock.patch.dict(os.environ     ,       {'WANDB_MODE': 'offline'}		)
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_auto_remove_tmp_dir()
									_UpperCAmelCase											=     f'''
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        '''.split()

									run_command(self._launch_args + testargs		)
									_UpperCAmelCase											=     get_results(_lowercase		)
									self.assertGreaterEqual(result['eval_accuracy']     ,       0.8		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'swag_no_trainer'		)		)		)
		@slow
		@mock.patch.dict(os.environ     ,       {'WANDB_MODE': 'offline'}		)
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_auto_remove_tmp_dir()
									_UpperCAmelCase											=     f'''
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

									run_command(self._launch_args + testargs		)
									_UpperCAmelCase											=     get_results(_lowercase		)
									self.assertGreaterEqual(result['eval_rouge1']     ,       10		)
									self.assertGreaterEqual(result['eval_rouge2']     ,       2		)
									self.assertGreaterEqual(result['eval_rougeL']     ,       7		)
									self.assertGreaterEqual(result['eval_rougeLsum']     ,       7		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'epoch_0'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'summarization_no_trainer'		)		)		)
		@slow
		@mock.patch.dict(os.environ     ,       {'WANDB_MODE': 'offline'}		)
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_auto_remove_tmp_dir()
									_UpperCAmelCase											=     f'''
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

									run_command(self._launch_args + testargs		)
									_UpperCAmelCase											=     get_results(_lowercase		)
									self.assertGreaterEqual(result['eval_bleu']     ,       30		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'epoch_0'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'translation_no_trainer'		)		)		)
		@slow
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     logging.StreamHandler(sys.stdout		)
									logger.addHandler(_lowercase		)

									_UpperCAmelCase											=     self.get_auto_remove_tmp_dir()
									_UpperCAmelCase											=     f'''
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        '''.split()

									run_command(self._launch_args + testargs		)
									_UpperCAmelCase											=     get_results(_lowercase		)
									self.assertGreaterEqual(result['eval_overall_accuracy']     ,       0.10		)






		@mock.patch.dict(os.environ     ,       {'WANDB_MODE': 'offline'}		)
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_auto_remove_tmp_dir()
									_UpperCAmelCase											=     f'''
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        '''.split()

									if is_cuda_and_apex_available():
																testargs.append('--fp16'		)

									run_command(self._launch_args + testargs		)
									_UpperCAmelCase											=     get_results(_lowercase		)
									# The base model scores a 25%
									self.assertGreaterEqual(result['eval_accuracy']     ,       0.6		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'step_1'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_lowercase     ,       'image_classification_no_trainer'		)		)		)



import math


def 					_UpperCAmelCase				(						a__ = 1_0_0):

			'''simple docstring'''



			a_						:						List[str] 							= sum(i * i for i in range(1  ,	n + 1))
			a_						:						Optional[Any] 							= int(math.pow(sum(range(1  ,	n + 1))  ,	2))
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
	print(F"""{solution() = }""")


